{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DAY5 简单线性回归（Simple Linear Regression）\n",
    "\n",
    "![0_0plB5_YzkViGnD3j.gif](0_0plB5_YzkViGnD3j.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: 预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入必要的库\n",
    "# 提示: 需要导入 pandas, numpy, matplotlib.pyplot\n",
    "\n",
    "# 从 'scores.csv' 文件中读取数据集\n",
    "# 提示: 使用 pandas 的 read_csv 方法\n",
    "\n",
    "# 将数据集划分为特征 X 和目标变量 Y\n",
    "# 提示: 使用 pandas 的 iloc 方法进行划分\n",
    "# X 应包含第一列, Y 应包含第二列\n",
    "\n",
    "# 导入 train_test_split 方法，用于划分训练集和测试集\n",
    "# 提示: 需要从 sklearn.model_selection 导入 train_test_split\n",
    "\n",
    "# 使用 train_test_split 方法将数据划分为训练集和测试集\n",
    "# 提示: 设置 test_size 为 1/4, random_state 为 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.5</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.5</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.5</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6.5</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.5</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.5</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9.5</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10.0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hours  Scores\n",
       "0     0.5       5\n",
       "1     1.0      10\n",
       "2     1.5      14\n",
       "3     2.0      20\n",
       "4     2.5      22\n",
       "5     3.0      25\n",
       "6     3.5      30\n",
       "7     4.0      35\n",
       "8     4.5      40\n",
       "9     5.0      42\n",
       "10    5.5      44\n",
       "11    6.0      50\n",
       "12    6.5      55\n",
       "13    7.0      60\n",
       "14    7.5      65\n",
       "15    8.0      68\n",
       "16    8.5      72\n",
       "17    9.0      75\n",
       "18    9.5      80\n",
       "19   10.0      85"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入线性回归模型\n",
    "# 提示: 使用 sklearn.linear_model 中的 LinearRegression\n",
    "\n",
    "# 创建一个 LinearRegression 实例\n",
    "# 提示: 实例化 LinearRegression，命名为 regressor\n",
    "\n",
    "# 使用训练数据拟合线性回归模型\n",
    "# 提示: 调用 regressor 的 fit 方法，传入 X_train 和 Y_train\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Step 3: 预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用线性回归模型对测试集进行预测\n",
    "# 提示: 调用 regressor 的 predict 方法，传入 X_test，并将结果赋值给 Y_pred\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Step 4: 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用 matplotlib 绘制散点图，显示训练集的实际数据点\n",
    "# 提示: 使用 plt.scatter 方法，X_train 作为 x 轴数据，Y_train 作为 y 轴数据，设置颜色为红色，添加标签 'Actual points'\n",
    "\n",
    "# 绘制回归线，显示训练集的预测结果\n",
    "# 提示: 使用 plt.plot 方法，X_train 作为 x 轴数据，使用 regressor 的 predict 方法对 X_train 进行预测作为 y 轴数据，设置颜色为蓝色，添加标签 'Regression line'\n",
    "\n",
    "# 为图表添加标题和轴标签\n",
    "# 提示: 使用 plt.title 添加标题 'Scores by Hours of Study (Training set)'\n",
    "# 使用 plt.xlabel 添加 x 轴标签 'Hours of Study'\n",
    "# 使用 plt.ylabel 添加 y 轴标签 'Scores'\n",
    "\n",
    "# 添加图例，区分散点图和回归线\n",
    "# 提示: 使用 plt.legend 方法\n",
    "\n",
    "# 添加网格线以便更好地阅读图表\n",
    "# 提示: 使用 plt.grid 方法，并设置参数 True\n",
    "\n",
    "# 显示图表\n",
    "# 提示: 使用 plt.show 方法\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用 matplotlib 绘制散点图，显示测试集的实际数据点\n",
    "# 提示: 使用 plt.scatter 方法，X_test 作为 x 轴数据，Y_test 作为 y 轴数据，设置颜色为红色，添加标签 'Actual points'\n",
    "\n",
    "# 绘制回归线，显示测试集的预测结果\n",
    "# 提示: 使用 plt.plot 方法，X_test 作为 x 轴数据，使用 regressor 的 predict 方法对 X_test 进行预测作为 y 轴数据，设置颜色为蓝色，添加标签 'Regression line'\n",
    "\n",
    "# 为图表添加标题和轴标签\n",
    "# 提示: 使用 plt.title 添加标题 'Scores by Hours of Study (Test set)'\n",
    "# 使用 plt.xlabel 添加 x 轴标签 'Hours of Study'\n",
    "# 使用 plt.ylabel 添加 y 轴标签 'Scores'\n",
    "\n",
    "# 添加图例，区分散点图和回归线\n",
    "# 提示: 使用 plt.legend 方法\n",
    "\n",
    "# 添加网格线以便更好地阅读图表\n",
    "# 提示: 使用 plt.grid 方法，并设置参数 True\n",
    "\n",
    "# 显示图表\n",
    "# 提示: 使用 plt.show 方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
